EP1624801A1 - Procede d'estimation du resultat d'un accident cerebrovasculaire par eeg - Google Patents

Procede d'estimation du resultat d'un accident cerebrovasculaire par eeg

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Publication number
EP1624801A1
EP1624801A1 EP04730944A EP04730944A EP1624801A1 EP 1624801 A1 EP1624801 A1 EP 1624801A1 EP 04730944 A EP04730944 A EP 04730944A EP 04730944 A EP04730944 A EP 04730944A EP 1624801 A1 EP1624801 A1 EP 1624801A1
Authority
EP
European Patent Office
Prior art keywords
stroke
patient
power
eeg
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP04730944A
Other languages
German (de)
English (en)
Inventor
Simon Peter Centre Magnetic Resonance FINNIGAN
Jonathan Brandon Centre Magnetic Resonance CHALK
Stephen Edward Centre Magnetic Resonance ROSE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Queensland UQ
Original Assignee
University of Queensland UQ
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Queensland UQ filed Critical University of Queensland UQ
Publication of EP1624801A1 publication Critical patent/EP1624801A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging

Definitions

  • This invention relates to a method of predicting the evolution and clinical outcome of a stroke or similar ischaemic infarction, using EEG measures acquired in the acute phase of a stroke, i.e. obtained shortly after the onset of stroke symptoms.
  • a person suffers an ischaemic infarction or stroke when a blood vessel is blocked, causing cerebral nervous tissue to be deprived of oxygen.
  • cerebral nervous tissue In the initial few hours after a stroke, there is usually a significantly reduced blood supply to a region of nervous tissue due to a blocked or nearly-blocked blood vessel which would otherwise supply oxygen to that tissue.
  • the nervous tissue deprived of adequate blood supply does not necessarily die immediately. It can often die over the next 18 hours or so.
  • the prediction of the final size of the stroke i.e. the final volume of dead tissue
  • patients' clinical outcomes is very difficult.
  • a principal challenge in acute stroke therapy is to accurately identify, monitor, and predict the progression of stroke evolution. If these objectives can be achieved, the patient can receive optimal treatment. The efficacy of drugs can also be evaluated.
  • Quantitative EEG (qEEG) techniques include the computation of power and associated scalp topographic maps, for given frequency bands. Such techniques have been used during the past three decades to illustrate, diagnose, and investigate brain pathophysiology following stroke, and the efficacy of qEEG in this context has been well- demonstrated. For example, in the sub-acute post-stroke period qEEG topographic maps have been shown to indicate pathophysiological foci before they are detectable in computed tomography (CT) scans, and such foci have been demonstrated to reliably correlate with the location of the lesion as indicated by CT and MRI.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • DWI diffusion-
  • PWI perfusion-weighted
  • MRI is not particularly practicable for continuous systematic investigations of the development of brain pathophysiology over the course of hours in the crucial acute post-stroke period (wherein current thrombolytic drugs, for example, must be administered).
  • This invention provides a method of predicting neurological developments resulting from a cerebral disorder in a patient, comprising the steps of acquiring EEG measures from the patient at at least two time-points, processing the acquired EEG measures to obtain a delta band power measure at each of the two time-points, and predicting clinical status of the patient from the change in the delta band power measure between the two time-points.
  • the two time-points at which the EEG measures are acquired are in the acute post-stroke period.
  • the acquired EEG measures are processed to obtain a delta band power change measure, known as the acute delta change index (aDCI).
  • aDCI acute delta change index
  • Subsequent clinical outcome in the patient is predicted on the basis of the aDCI and the patients' clinical status on hospital admission.
  • the EEG data used to calculate the aDCI are acquired from the patient within approximately 18 hours, and commencing within 7 hours, of the onset of stroke-associated symptoms in the patient.
  • Such data should be acquired from at least 20 electrodes distributed evenly across the scalp of the patient (and delta power and the aDCI will generally be greatest in EEG data from electrodes placed on regions of the scalp overlying the stroke).
  • the acquired EEG data are suitably processed, e.g. with appropriate computerised algorithms, to obtain a power spectrum in the delta band, which for the present purposes, is 1 - 4Hz.
  • the power spectrum is computed by a Fast Fourier Transform on artefact- free portions of EEG data.
  • the aDCI indexing both the direction and mean proportional change per hour in average scalp delta power, can then be used to predict clinical outcome for the stroke patient.
  • Multi-channel EEG data is acquired within the acute phase of stroke (and commencing in the hyper-acute post-stroke period) using scalp electrodes, and artifact free periods of EEG data are selected.
  • the artifact free EEG data is bandpass filtered, separated into contiguous segments, and frequency band power is calculated for each electrode at a series of frequency points over a frequency range using the Fast Fourier Transform.
  • An "average scalp power spectrum” is computed by calculating the mean power (at each frequency point) across all scalp electrodes. 4.
  • the frequency at which peak power occurs is determined in each patient's average scalp power spectrum and is verified in power spectra from several electrodes overlying the location of the stroke in the brain. This frequency is in the delta band (1-4 Hz), but varies from patient to patient. The power associated with this peak frequency is determined for each portion of artifact-free EEG data.
  • delta power can be computed over the frequency interval between 1Hz below peak delta power [but not below 1Hz as the lower limit due to low-frequency EEG artifacts] and 1Hz above peak delta power.
  • This delta power metric in data from one time-point is then subtracted from the same metric obtained at the next such time-point, and this difference score is divided by the time (in hours) that had elapsed between these two time-points in order to calculate the slope of a line constituting the cross-temporal change in delta power.
  • the recommended minimum elapsed time between these two delta power metrics should generally be 3 hours.
  • the resulting slope value is then converted to a quotient of the original delta power metric, producing the aDCI.
  • Reduction in delta power as recorded on EEG can provide an indication of subsequent clinical improvement in stroke patients over the following hours and up to at least 30days post-stroke.
  • EEG coherence both linear and non-linear
  • EEG coherence may also be used to predict clinical and functional outcomes from stroke.
  • EEG delta changes and/or other EEG metrics
  • MRI data obtained from the same patient at the same time will likely improve prediction of stroke outcome.
  • the method of this invention has several advantages.
  • First, the derivatives of data acquired from acute stroke patients allow prediction of the effects and efficacy of putative neuroprotective and thrombolytic agents, thereby enhancing the efficacy of the clinical management of acute stroke patients.
  • cross-temporal qEEG correlates of brain pathophysiology in the acute post-stroke period can be employed to generate an electrophysio logical predictive model of stroke evolution and functional outcome.
  • EEG data can be continuously collected from patients whilst in their hospital beds (including in emergency wards or intensive care units), and this can be acquired relatively readily due to low relative cost and rather widespread availability of EEG equipment.
  • the method is particularly suitable for predicting neurological developments resulting from a stroke or like cerebral ischaemia, it could be likewise applied to prediction of subsequent clinical status in conditions such as coma, brain haemorrhage, traumatic brain injury, and brain tumour.
  • Figure. 1 illustrates EEG and MRI data from a patient who made an excellent early recovery.
  • a and B axial and left lateral EEG scalp Delta Power Maps (DPM) acquired 6.5 hours after onset of symptoms; C: initial DWI (6 hours); D: initial MTT map; E and F: axial and lateral DPM at 13 hours; G: 15 hour DWI scan and H: 30 day T2 MRI.
  • DPM EEG scalp Delta Power Maps
  • Figure. 2 illustrates EEG and MRI data from a patient who died at 12 days post-stroke.
  • a and B axial and lateral DPM acquired 9 hours after onset of symptoms;
  • C initial DWI (6 hours);
  • D initial MTT map;
  • E and F axial and right lateral DPM at 17 hours;
  • G 15 hour DWI scan and H: 15 hour T2 MRI.
  • FIG. 3 illustrates EEG and MRI data from a patient who received recombinant tissue Plasminogen Activator.
  • a and B axial and left lateral DPM acquired 6 hours after onset of symptoms; C: initial DWI (4.5 hours); D: initial MTT map; E and F: axial and lateral DPM at 12 hours; G: 13 hour DWI scan and H: 30 day T2 MRI.
  • the NIHSS score was used to evaluate neurological impairment from stroke.
  • the scale is an 11 -item, clinical evaluation instrument widely used in clinical trials and practice, the reliability and validity of which is widely documented.
  • the scale was administered on admission and at 24 ⁇ 2 hours, 48 ⁇ 2 hours, 72 ⁇ 2 hours, and 30 ⁇ 2days, post-stroke.
  • Electrode locations corresponded to the following sites of the International 10-20 system: FPz, FP1, FP2, AF3, AF4, AF7, AF8, Fz, FI, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, CI, C2, C3, C4, C5, C6, T7, T8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, PI, P2, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, Ol, O2.
  • the use of electrode caps is not essential, nor is such a high-
  • Electrode impedances were predominantly 10-20 k ⁇ or less. EEG data was filtered (bandpass; 0.01-100 Hz) online and digitised at a sampling rate of 500 Hz. Recordings were made using a Neuroscan SynAmps 64 channel digital EEG amplification and acquisition system.
  • EEG was acquired continuously from the earliest practicable time post-MRI scan (approximately 7 ⁇ 2 hours post-stroke) until 15 ⁇ 2 hours post-stroke. At least several minutes of artefact-free EEG data was acquired within both the first and last hours of recording, during which times the patient was awake but resting quietly and still with eyes closed, with zero or minimal ambient noise and other activity in the room or immediate vicinity. In addition, between 20 and 30 minutes of EEG data were acquired under those conditions at 48 ⁇ 2 hours, and 30 ⁇ 2 days, post-stroke.
  • EEG bandpower (representing voltage amplitude squared) was calculated for each electrode and at each 0.25 Hz point (over the range 0.5-40 Hz), using the Fast Fourier Transform.
  • an "average scalp power spectrum” was computed by calculating the mean power (at each frequency point) across all 62 scalp electrodes.
  • the frequency at which peak power occurred was determined in each patient's average scalp power spectrum. This frequency was always in the delta band (1-4 Hz), but varied between 1 and 1.75 Hz from patient to patient. The power associated with this peak frequency was determined for each portion of high-quality EEG data. (In alternative embodiments, instead of, or in addition to peak delta power, delta power in the range l-4Hz, and/or from 1Hz below peak to 1Hz above peak, may be used.)
  • this delta power metric in data from the first high-quality EEG data time-point was subtracted from the same metric obtained at the second such time-point.
  • the difference score was then divided by the time (in hours) that had elapsed between these two time-points in order to calculate the slope of a line constituting the cross-temporal change in delta power.
  • the resulting slope value was then converted to a quotient of the original delta power metric, and this quotient was subsequently correlated with patients' clinical outcomes as indexed by the NIHSS score at 30 days post-stroke. That function was also computed as the change between time points one and two, divided by the elapsed time between these, and then by the original NIHSS score.
  • changes in delta power can be used to predict likely stroke development.
  • the aDCI provides a quantitative measure of expected stroke outcome. Where drug therapy is applied, the efficacy of such therapy can be evaluated by reference to predicted functional outcome.
  • Calculation of the aDCI can be computerised or automated, using appropriate software.
  • the predictive approach may use regression, a statistical procedure that regularly follows that of correlation.
  • a "line of best fit” or “regression line” is plotted to the data, using the Least Squares criterion. (This can be computed easily via any one of a number of standard computerised statistical packages).
  • the subsequent NIHSS score at 30 days post- stroke (termed Y) can then be predicted on the basis of three variables:
  • Y bX + a Predictive example based upon the twelve patients' data illustrated in Fig 4:
  • EEG data is recorded from 7 hours to 13 hours post-stroke (with 3 minutes of high- quality, artefact-free data acquired at start and end)
  • the slope of the "delta change” line is calculated as -9.37 (i.e. [26.5 - 82.7] divided by 6 [the number of hours elapsed]) •
  • the aDCI is calculated as -0.11 (i.e., the delta slope value, -9.37, divided by the initial delta power value of 82.7); this serves as the X- value for the regression equation
  • the predicted NIHSS score at 30 days, Y is calculated using the abovementioned regression equation as -0.42 (i.e., 3.27 multiplied by -9.37, plus -0.05)
  • This NIHSS change represents the NIHSS score at the latter time-point minus the initial such score, and the difference divided by the initial score.
  • the predicted NIHSS at 30 days post-stroke would in this case be 14.5
  • the general methodology of this invention may be applied to other disorders including cerebrovascular disorders (such as brain haemorrhages, various forms of hypoxia [disruption of regular oxygen supply to the brain], and severe migraines), coma states, and brain tumours, all of which elicit similar EEG outcomes to stroke (e.g., pronounced slowing of the brain's electrical oscillations, leading to high delta power), as well traumatic brain injuries and possibly mild traumatic brain injuries such as concussion.
  • Related analysis & prognostic strategies (which might in future take into account EEG frequency bands other than delta) might be applied to other brain disorders such as mild cognitive impairment (a precursor of Alzheimers disease and other dementias) and epilepsy.
  • qEEG aDCI acute delta change index

Abstract

Les mesures de l'électroencéphalogramme (EEG) servent à estimer les développements neurologiques consécutifs à un accident cérébrovasculaire ou tout autre ischémie cérébrale chez un sujet. L'acquisition des mesures de l'EEG se fait à deux moments précis de la phase aiguë de l'accident cardiovasculaire et dans les 18 heures suivent l'apparition des premiers symptômes, l'acquisition débutant dans les 7 heures après les premiers symptômes. Le traitement des mesures de l'EEG acquises permet d'obtenir une mesure de changement de puissance de bande delta, appelée indice aDCI (acute delta change index). Les résultats cliniques ultérieurs (par exemple, 30 jours après l'accident cardiovasculaire) sont estimés sur la base de l'aDCI.
EP04730944A 2003-05-02 2004-05-04 Procede d'estimation du resultat d'un accident cerebrovasculaire par eeg Withdrawn EP1624801A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2003902115A AU2003902115A0 (en) 2003-05-02 2003-05-02 Method of predicting functional outcome of a stroke using eeg measures
PCT/AU2004/000578 WO2004096040A1 (fr) 2003-05-02 2004-05-04 Procede d'estimation du resultat d'un accident cerebrovasculaire par eeg

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EP1624801A1 true EP1624801A1 (fr) 2006-02-15

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US (1) US20070032736A1 (fr)
EP (1) EP1624801A1 (fr)
AU (1) AU2003902115A0 (fr)
WO (1) WO2004096040A1 (fr)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130253362A1 (en) * 2008-04-15 2013-09-26 Christopher Scheib Method and system for monitoring and displaying physiological conditions
US8241221B2 (en) * 2009-02-05 2012-08-14 Pacesetter, Inc. Systems and methods for use with an implantable medical device for detecting stroke based on electrocardiac signals
US8467864B2 (en) * 2010-03-11 2013-06-18 Pacesetter, Inc. Systems and methods for use by an implantable medical device for detecting and discriminating stroke and cardiac ischemia using electrocardiac signals and hemodynamic parameters
CA2798122A1 (fr) * 2010-04-30 2011-11-03 Diadexus, Inc. Methodes utilisant la phospholipase a2 associee aux lipoproteines dans une unite de soins de courte duree
US20150302155A1 (en) * 2014-04-16 2015-10-22 Xerox Corporation Methods and systems for predicting health condition of human subject
US20210030299A1 (en) * 2018-04-06 2021-02-04 The Board Of Trustees Of The Leland Stanford Junior University Apparatus and methods for detection of the onset and monitoring the progression of cerebral ischemia to enable optimal stroke treatment
EP3908179A4 (fr) * 2019-01-08 2022-10-26 Neuroindex Ltd. Synchronisation de l'activité cérébrale
WO2024035887A1 (fr) * 2022-08-12 2024-02-15 The Regents Of The University Of California Méthodes et systèmes de détection d'accident vasculaire cérébral

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5269315A (en) * 1991-08-16 1993-12-14 The Regents Of The University Of California Determining the nature of brain lesions by electroencephalography
RU2039524C1 (ru) * 1991-11-21 1995-07-20 Нина Васильевна Дмитриева Способ оценки функционального состояния центральной нервной системы
US5267570A (en) * 1992-12-30 1993-12-07 Preston Myra S Method of diagnosing and treating chronic fatigue syndrome
US6013792A (en) * 1993-08-05 2000-01-11 Syntex (U.S.A.), Inc. Matrix metalloprotease inhibitors
US5445162A (en) * 1993-08-27 1995-08-29 Beth Israel Hospital Association Apparatus and method for recording an electroencephalogram during magnetic resonance imaging
DE19725214C1 (de) * 1997-06-15 1998-11-19 Mario Dr Siebler Vorrichtung zum Messen und Analysieren elektrischer Hirnströme
US6622036B1 (en) * 2000-02-09 2003-09-16 Cns Response Method for classifying and treating physiologic brain imbalances using quantitative EEG
EP1017307B1 (fr) * 1997-09-23 2010-05-05 Natus Medical Incorporated Dispositif de detection d'atteintes neurales affectant la substance blanche et de prevision de l'evolution neurologique notamment dans le cas des prematures
US6223074B1 (en) * 1999-08-10 2001-04-24 Thuris Corporation Method and computer program product for assessing neurological conditions and treatments using evoked response potentials
US6280393B1 (en) * 1999-12-02 2001-08-28 Thuris Corporation Method and apparatus for assessing susceptibility to stroke
US6985769B2 (en) * 2001-02-13 2006-01-10 Jordan Neuroscience, Inc. Automated realtime interpretation of brain waves
AUPR358701A0 (en) * 2001-03-07 2001-04-05 University Of Queensland, The Method of predicting stroke evolution
JP2003052656A (ja) * 2001-08-20 2003-02-25 Fuji Xerox Co Ltd 脳波解析装置、脳波解析方法、脳波解析プログラム及びコンピュータ読み取り可能な記録媒体
US7024238B2 (en) * 2003-04-16 2006-04-04 New England Medical Center Hospitals, Inc. Detecting ischemia

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO2004096040A1 *

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Publication number Publication date
US20070032736A1 (en) 2007-02-08
WO2004096040A1 (fr) 2004-11-11
AU2003902115A0 (en) 2003-05-22

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